Hasil untuk "Diseases of the musculoskeletal system"

A. Rausch Osthoff, K. Niedermann, J. Braun et al.

Regular physical activity (PA) is increasingly promoted for people with rheumatic and musculoskeletal diseases as well as the general population. We evaluated if the public health recommendations for PA are applicable for people with inflammatory arthritis (iA; Rheumatoid Arthritis and Spondyloarthritis) and osteoarthritis (hip/knee OA) in order to develop evidence-based recommendations for advice and guidance on PA in clinical practice. The EULAR standardised operating procedures for the development of recommendations were followed. A task force (TF) (including rheumatologists, other medical specialists and physicians, health professionals, patient-representatives, methodologists) from 16 countries met twice. In the first TF meeting, 13 research questions to support a systematic literature review (SLR) were identified and defined. In the second meeting, the SLR evidence was presented and discussed before the recommendations, research agenda and education agenda were formulated. The TF developed and agreed on four overarching principles and 10 recommendations for PA in people with iA and OA. The mean level of agreement between the TF members ranged between 9.8 and 8.8. Given the evidence for its effectiveness, feasibility and safety, PA is advocated as integral part of standard care throughout the course of these diseases. Finally, the TF agreed on related research and education agendas. Evidence and expert opinion inform these recommendations to provide guidance in the development, conduct and evaluation of PA-interventions and promotion in people with iA and OA. It is advised that these recommendations should be implemented considering individual needs and national health systems.

Ningning Feng, Shuyin Tan, Xing Yu et al.

Abstract Purpose A systematic review of animal models for endplate injury was conducted to identify an appropriate model for investigating the pathophysiological mechanisms underlying endplate injury and its association with low back pain (LBP). Methods A comprehensive search of relevant literature was conducted using electronic databases. The identified studies were then evaluated based on predefined inclusion and exclusion criteria. Subsequently, key information and primary experimental findings from the selected literature were extracted and synthesized. This research was performed in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results Our study incorporated a total of 10 papers, covering a span of 8 years. Among these, 2 studies specifically induced injury to the endplate and intervertebral disc, respectively. 6 studies involved puncturing the endplate or intervertebral disc followed by the injection of various substances. Additionally, 1 study focused on modeling lumbar instability, while another modeled the tamponade of the nucleus pulposus within the vertebral body. The subjects primarily consisted of mice and rabbits, with 2 studies utilizing mice and 5 studies selecting rats. The remaining 3 studies employed New Zealand White rabbits. The observation period for the experimental models ranged from 4 to 24 weeks postoperatively. The assessment of the animal models predominantly included imaging, behavioral evaluations, histological analyses, and molecular biological tests, with MRI and histological examination being the most frequently utilized methods. Conclusions The research has found that endplate Modic changes and endplate injuries are closely related to LBP. Animal experiments provide a model reference for exploring the intrinsic mechanism between the two and the corresponding therapeutic options. Although different modeling methods all induce varying degrees of endplate damage, each has its own advantages and disadvantages. The occurrence process of diseases in animal models is not completely equivalent to that in the human body, and there is currently no unified standard for clinical judgment of such models. Therefore, when choosing an animal model, multiple considerations should be integrated.

Yao-Yi Kuo, Si-Qi Chiu, Wen-Pei Chang et al.

Abstract Background Full-thickness cartilage defects have a significant impact on the function of joints in young adults, and the treatment of cartilage defects has been a challenge, as cartilage tissue is an avascular tissue. This study aimed to compare the clinical and radiological outcomes of Biphasic Cartilage Repair Implant (BiCRI) and microfracture treatments for knee cartilage defects. Methods This randomized controlled clinical trial enrolled patients with symptomatic knee chondral lesions smaller than 3 cm2. They were randomized to either the BiCRI (n = 11) or microfracture (n = 10) treatment groups. BiCRI or microfracture surgical procedures were performed on the patients, who were subsequently followed for a period of five years. Primary outcome measures included the International Knee Documentation Committee (IKDC) score, Knee Injury and Osteoarthritis Outcome Score (KOOS), Visual Analog Scale (VAS) score, Magnetic Resonance Imaging (MRI) measured cartilage thickness, and the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. Results 21 patients were enrolled, who were subsequently followed for a period of five years. Both BiCRI and microfracture treatments demonstrated significant improvements in IKDC, KOOS and VAS scores, with no significant differences between the two. MRI analysis indicated a significant increase in minimum cartilage thickness with BiCRI treatment (median of difference: 1 mm, P = 0.026)), in contrast to the nonsignificant change in the microfracture group (median of difference: 1 mm, P = 0.102). The MOCART scores revealed a significant increase percentage of isointense signal intensity identical to the adjacent articular cartilage (P = 0.03) in the BiCRI group from the 2-year to the 5-year mark, while the scores remained stable in the microfracture group. Moreover, the BiCRI technique displayed superior performance in graft infill at 5 years (P = 0.008), border integration at 5 years (P = 0.04), surface contour at 2 years (P = 0.04) compared to microfracture. Conclusions Both BiCRI and microfracture treatments showed significant effectiveness in improving clinical outcomes in patients with small symptomatic articular cartilage defects of the knee, with the BiCRI group demonstrating a superior radiological outcome than microfracture, over a five-year period. However, the sample size of our study is relatively small to reach a definite conclusion, and further studies with larger sample size and longer follow up are recommended. Trial registration The trial was registered on ClinicalTrials.gov under the identifier NCT01477008.

Lucrezia Zuccarelli, Maria De Martino, Antonio Filippi et al.

ABSTRACT Background We recently demonstrated that following a 10‐day exposure to inactivity/simulated microgravity impairments of oxidative metabolism were located ‘upstream’ of mitochondrial function, as evaluated by maximal ADP‐stimulated mitochondrial respiration (JO2max) determined ex vivo. The aim of this study was to evaluate mitochondrial sensitivity to submaximal [ADP] by an alternative approach aimed at identifying responses associated with fibre type composition. Methods Isolated permeabilized vastus lateralis fibres were analysed by high‐resolution respirometry in 9 young males before and after a 10‐day horizontal bed rest. Eleven submaximal titrations of ADP (from 12.5 to 10 000 μM) were utilized to assess complex I + II‐linked ADP sensitivity. We applied to JO2 versus [ADP] data a traditional Michaelis–Menten kinetics equation, with the calculation of the apparent Km and maximal respiration (Vmax), and two ‘sequential’ hyperbolic equations, yielding two Km and Vmax values. The two‐hyperbolic equations were solved and the [ADP] value corresponding to 50% of JO2max was calculated. Isoform expression of myosin heavy chains (MyHC) 1, 2A and 2X was also determined. Control experiments were also carried out on rat skeletal muscle samples with different percentages of MyHC isoforms. Results The two hyperbolic equations provided an alternative fitting of data and identified two distinct phases of the JO2 versus [ADP] response: a first phase characterized by low Vmax (Vmax1, 28 ± 10 pmol s−1 mg−1) and apparent Km (Km1, 62 ± 54 μM) and a second phase characterized by higher Vmax (Vmax2, 61 ± 16 pmol s−1 mg−1) and Km (Km2, 1784 ± 833 μM). Data were confirmed in control experiments carried out in rat muscle samples with different percentages of MyHC isoforms. Correlation and receiver operating characteristics analyses suggest that the two phases of the response were related to the % of MyHC isoforms. Conclusions A novel mathematical approach (two sequential hyperbolic functions) for the fitting of JO2 versus [ADP] data obtained by high‐resolution respirometry on permeabilized skeletal muscle fibres, obtained in humans and rats, provided an alternative fitting of the experimental data compared to the traditional Michaelis–Menten kinetics equation. This alternative model allowed the identification of two distinct phases in the responses, which were related to fibre type composition. A first phase, characterized by low apparent Km and Vmax values, was correlated with the percentage of less oxidative (Type 2A + 2X) MyHC isoforms. A second phase, characterized by high apparent Km and Vmax, was related to more oxidative (Type 1) MyHC isoforms.

Qian Liu, Haozhe Cui, Fei Si et al.

ABSTRACT Background Previous studies have demonstrated that metabolic score for visceral fat (METS‐VF), a novel surrogate indicator assessing visceral fat, was associated with the risk of hypertension, diabetes mellitus, cardiovascular disease (CVD) and mortality, predicting the risks based on a single METS‐VF measurement can increase limitations of the study. Few studies have investigated the association between cumulative exposure to METS‐VF and risk of CVD and all‐cause mortality. We aimed to examine the association of cumulative METS‐VF with risk for CVD and all‐cause mortality. Methods All participants in the study were from the Kailuan Study, which is a large, prospective cohort study, and began in 2006 years. Cumulative METS‐VF was calculated by data from 2006 survey to 2010 survey and defined as the mean METS‐VF for each pair of consecutive surveys multiplied by the time intervals between these two consecutive surveys. The optimal cut‐off value for time‐averaged cumulative METS‐VF associated with CVD was determined using a survival‐time method to calculate maximally selected rank statistics and was used to assess exposure of high METS‐VF. A multivariate Cox proportional hazards regression model was used to assess the risk of CVD and all‐cause mortality during 2010–2022 years (hazard ratio [HR] and 95% confidence interval [95% CI]). Results We included 41 756 participants (mean age, [52.72 ± 11.64] years, 78.53% males and 21.47% females). All participants were divided into four groups: Q1 (reference group), Q2, Q3 and Q4 according to the quartiles of cumulative METS‐VF, and exposure duration of high METS‐VF was quantified as 0, 2, 4, and 6 years. During the median follow‐up of 12.01 years, 4008 (9.60%) CVD events and 3944 all‐cause mortality events occurred. After adjusting for potential covariates, compared to participants in Q1 group, the HRs of incident CVD and all‐cause mortality were 1.55 (95% CI, 1.38–1.74) and 1.59 (95% CI, 1.40–1.81) for those in Q2 group, 2.13 (95% CI, 1.91–2.38) and 2.67 (95% CI 2.37–3.01) for those in Q3 group, 2.78 (95% CI, 2.49–3.17) and 4.90 (95% CI 4.36–5.50) for those in Q4 group. The HRs for CVD and all‐cause mortality were increased with exposure duration of high METS‐VF increasing. The result of ROC curve analysis showed that cumulative METS‐VF had the highest predictive for CVD among 4 indexes including cumulative METS‐VF, cumulative waist circumference, cumulative body mass index and cumulative WHtR. Conclusions The high cumulative METS‐VF was associated with an increased risk of CVD and mortality, and this association was stronger as exposure to high METS‐VF was prolonged, emphasizing the importance of striving to control the METS‐VF.

Aurora Hirvonen, S. Comero, Simona Tavazzi et al.

The Encyclopaedia Cloacae is a novel and centralised digital platform designed to support and advance wastewater-based epidemiology (WBE) by cataloguing pathogens detectable in wastewater and their relevance to public health surveillance. The platform is hosted on the EU Wastewater Observatory for Public Health (EU4S) website, where it is populated with peer-reviewed research through a structured workflow under harmonised criteria which address the presence of pathogens in human excreta, detectability in wastewater, and integration into public health systems. This tri-criteria approach ensures that the database is both scientifically robust and operationally actionable. Complemented by the Visualising the Invisible dashboard, the platform offers geospatial insights into global WBE research activity. By consolidating peer-reviewed evidence on pathogen detectability in wastewater and human excreta, the Encyclopaedia Cloacae enables early detection of infectious diseases, whether already known or newly emerging. The continuously updated repository and geospatial dashboards help to identify surveillance gaps and research hotspots, to support timely public health responses, enhance pandemic preparedness, and strengthen global health security. In addition, it supports One Health strategies, connecting the health of humans, animals, and the shared environment. This article outlines the platform’s architecture, data curation methodology, and future directions, including automation and expansion to encompass broader health determinants such as antimicrobial resistance and chemical hazards.

A. Faragalli, R. Franceschi, M. Marigliano et al.

Introduction Achieving and maintaining optimal glycemic control from the onset of type 1 diabetes (T1D) is crucial in pediatric care, especially in early childhood when the developing brain is highly vulnerable to both hypo- and hyperglycemia [1-3]. Hyperglycemia during early childhood increases the risk of long-term vascular complications, while severe hypoglycemia may impair neurocognitive development, causes family anxiety, and complicates social integration [4-5]. Although automated insulin delivery (AID) systems have demonstrated efficacy in controlled trials, real-world evidence in children under six years of age, particularly involving off-label use, remains limited. The Control-IQ (CIQ) algorithm, integrated into the Tandem t:slim X2 insulin pump, has shown benefits in adolescents and school-aged children [6-10]. However, few studies have evaluated its long-term use in children under six in routine clinical practice. Objective This study aimed to compare the real-world effectiveness and safety of the CIQ system in two pediatric age groups—children aged 0.5–5 years and children aged 6–10 years—over an 18-month follow-up period. We evaluated effectiveness in terms of glycemic control (% of time in glucose range 70–180 mg/dL [TIR], % of time in glucose range 70–140 mg/dL [TITR], and HbA1c) and safety in term of adverse events (diabetic ketoacidosis [DKA], hyperglycemia and severe hypoglycemia). Methods This prospective, multicenter observational study used retrospective data from 32 Italian pediatric diabetes centers. Eligible participants had T1D diagnosed  ≥ 6 months, , were 10 years at CIQ start were excluded. Participants were stratified by age at CIQ initiation (0.5–5 and 6–10 years). At CIQ initiation (baseline) sex, presence of celiac disease or thyroiditis and parents’ age, nationality and education, were collected. HbA1c, BMI z-score, CGM-derived data (TIR, TITR, % of time spent in glucose ranges: 250 mg/dL, Glucose Monitoring Indicators and coefficient of variation of glucose), Glycemia, Standard Deviation of Glycemia [SD] and DKA episodes were assessed at baseline, 6, 12, and 18 months. Descriptive statistics were used for baseline comparisons. Chi-square or t tests evaluated group differences. Trend over time points in TIR, TITR, and HbA1c were analysed using mixed-effects models for repeated measures, adjusted by age group, sex, time from diagnosis to CIQ initiation, DKA at onset and parents’ socio-economic characteristics (at least one non-Italian parent, parents’ education). A sequential difference contrast was used to model time; interaction between time and age groups was evaluated. Only children with complete data on the outcomes at all four time points were included in these models. Safety outcomes included the proportions of DKA and severe hypoglycaemias occurring during 18-month follow-up. Results Of the 334 children enrolled, 253 (106 aged 0.5–5; 147 aged 6–10) had complete data on the outcomes and were included in longitudinal analyses. At T1D diagnosis, a higher prevalence of thyroiditis in the older group was found, and no significant sociodemographic differences. At CIQ initiation, younger children had a significantly shorter time from diagnosis to CIQ initiation (1.36 vs 2.61 years, p<0.001), higher HbA1c (8.3%% vs 7.7%, p=0.020) and higher glycaemic variability (SD 63.3 mg/dL vs 58.3 mg/dL, p = 0.023) while TIR, TITR, and the other CGM-derived data were comparable. Longitudinal analysis (Figure 1) showed significant improvement in both groups 6 months after CIQ initiation: TIR increased by 6.62% (95% CI: 4.89–8.36) and TITR by 5.63% (95% CI: 3.61–7.66), corresponding to over 80 additional minutes/day spent in target ranges. These improvements were sustained at 12 and 18 months. HbA1c decreased by an average of 0.82% (95% CI: –1.01 to –0.62) in the first 6 months, remaining stable thereafter. No significant interaction between time and age groups was observed, indicating similar trends in both cohorts. Having at least one non-Italian parent was significantly associated with lower TIR (-5.82%, 95% CI: -10.33 to -1.31) and higher HbA1c levels (0.31%, 95% CI: 0.01 to 0.63). A high parental education level (university or higher vs. up to lower secondary education) was associated with higher TIR (8.61%, 95% CI: 3.03–14.18) and lower HbA1c levels (−0.42%, 95% CI: −0.78 to −0.06). Age at CIQ initiation, time from diagnosis to CIQ initiation, DKA at diagnosis, and sex were not significant predictor. Regarding safety, no severe hypoglycaemia episodes were reported in the younger group, and only one occurred in the older group after 12 months. A single DKA episode was recorded in a child under six. Moreover, CGM-derived data indicated that time spent in hypoglycaemia (<54 and 54–69 mg/dL) remained consistently below clinically relevant thresholds (<1% and <3%, respectively). Conclusion In this large real-world cohort of young children with T1D, the CIQ system demonstrated consistent and sustained improvements in glycaemic outcomes over 18 months, with minimal adverse events. Significant gains in TIR, TITR, and HbA1c were observed in both age groups, particularly in the first 6 months after CIQ initiation. These benefits were maintained long-term, regardless of initial glycaemic status and presence of DKA at diagnosis. The system proved safe even in children under six, supporting its current use in off-label settings with appropriate clinical oversight. Our findings reinforce the value of early AID adoption to optimize long-term metabolic outcomes in pediatric T1D.

Farnoosh Koleini, Hongfei Xue, Ahmed Helmy et al.

Reconstructing biomechanically realistic 3D human motion - recovering both kinematics (motion) and kinetics (forces) - is a critical challenge. While marker-based systems are lab-bound and slow, popular monocular methods use oversimplified, anatomically inaccurate models (e.g., SMPL) and ignore physics, fundamentally limiting their biomechanical fidelity. In this work, we introduce MonoMSK, a hybrid framework that bridges data-driven learning and physics-based simulation for biomechanically realistic 3D human motion estimation from monocular video. MonoMSK jointly recovers both kinematics (motions) and kinetics (forces and torques) through an anatomically accurate musculoskeletal model. By integrating transformer-based inverse dynamics with differentiable forward kinematics and dynamics layers governed by ODE-based simulation, MonoMSK establishes a physics-regulated inverse-forward loop that enforces biomechanical causality and physical plausibility. A novel forward-inverse consistency loss further aligns motion reconstruction with the underlying kinetic reasoning. Experiments on BML-MoVi, BEDLAM, and OpenCap show that MonoMSK significantly outperforms state-of-the-art methods in kinematic accuracy, while for the first time enabling precise monocular kinetics estimation.

Chanti Raju Mylay, Bobin Deng, Zhipeng Cai et al.

Crop diseases pose significant threats to global food security, agricultural productivity, and sustainable farming practices, directly affecting farmers' livelihoods and economic stability. To address the growing need for effective crop disease management, AI-based disease alerting systems have emerged as promising tools by providing early detection and actionable insights for timely intervention. However, existing systems often overlook critical aspects such as data privacy, market pricing power, and farmer-friendly usability, leaving farmers vulnerable to privacy breaches and economic exploitation. To bridge these gaps, we propose AgriSentinel, the first Privacy-Enhanced Embedded-LLM Crop Disease Alerting System. AgriSentinel incorporates a differential privacy mechanism to protect sensitive crop image data while maintaining classification accuracy. Its lightweight deep learning-based crop disease classification model is optimized for mobile devices, ensuring accessibility and usability for farmers. Additionally, the system includes a fine-tuned, on-device large language model (LLM) that leverages a curated knowledge pool to provide farmers with specific, actionable suggestions for managing crop diseases, going beyond simple alerting. Comprehensive experiments validate the effectiveness of AgriSentinel, demonstrating its ability to safeguard data privacy, maintain high classification performance, and deliver practical, actionable disease management strategies. AgriSentinel offers a robust, farmer-friendly solution for automating crop disease alerting and management, ultimately contributing to improved agricultural decision-making and enhanced crop productivity.

Vittorio La Barbera, Steven Bohez, Leonard Hasenclever et al.

We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.

Chanh Nguyen, Erik Elmroth, Monowar Bhuyan

Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional anomaly detection methods. These conventional approaches, designed for stateful and long-running services, struggle in serverless settings where executions are short-lived, functions are isolated, and observability is limited. In this first comprehensive vision paper on anomaly detection for serverless systems, we systematically explore the unique challenges posed by this paradigm, including the absence of persistent state, inconsistent monitoring granularity, and the difficulty of correlating behaviors across distributed functions. We further examine a range of threats that manifest as anomalies, from classical Denial-of-Service (DoS) attacks to serverless-specific threats such as Denial-of-Wallet (DoW) and cold start amplification. Building on these observations, we articulate a research agenda for next-generation detection frameworks that address the need for context-aware, multi-source data fusion, real-time, lightweight, privacy-preserving, and edge-cloud adaptive capabilities. Through the identification of key research directions and design principles, we aim to lay the foundation for the next generation of anomaly detection in cloud-native, serverless ecosystems.

Xinyang Zhou, Yongyong Ren, Qianqian Zhao et al.

Accurate diagnosis of Mendelian diseases is crucial for precision therapy and assistance in preimplantation genetic diagnosis. However, existing methods often fall short of clinical standards or depend on extensive datasets to build pretrained machine learning models. To address this, we introduce an innovative LLM-Driven multi-agent debate system (MD2GPS) with natural language explanations of the diagnostic results. It utilizes a language model to transform results from data-driven and knowledge-driven agents into natural language, then fostering a debate between these two specialized agents. This system has been tested on 1,185 samples across four independent datasets, enhancing the TOP1 accuracy from 42.9% to 66% on average. Additionally, in a challenging cohort of 72 cases, MD2GPS identified potential pathogenic genes in 12 patients, reducing the diagnostic time by 90%. The methods within each module of this multi-agent debate system are also replaceable, facilitating its adaptation for diagnosing and researching other complex diseases.

Soumyajit Maity, Pranjal Kamboj, Sneha Maity et al.

This paper proposes a MedGemma-based framework for automatic abnormality detection in musculoskeletal radiographs. Departing from conventional autoencoder and neural network pipelines, the proposed method leverages the MedGemma foundation model, incorporating a SigLIP-derived vision encoder pretrained on diverse medical imaging modalities. Preprocessed X-ray images are encoded into high-dimensional embeddings using the MedGemma vision backbone, which are subsequently passed through a lightweight multilayer perceptron for binary classification. Experimental assessment reveals that the MedGemma-driven classifier exhibits strong performance, exceeding conventional convolutional and autoencoder-based metrics. Additionally, the model leverages MedGemma's transfer learning capabilities, enhancing generalization and optimizing feature engineering. The integration of a modern medical foundation model not only enhances representation learning but also facilitates modular training strategies such as selective encoder block unfreezing for efficient domain adaptation. The findings suggest that MedGemma-powered classification systems can advance clinical radiograph triage by providing scalable and accurate abnormality detection, with potential for broader applications in automated medical image analysis. Keywords: Google MedGemma, MURA, Medical Image, Classification.

Sara Mansoorshahi, Cindy Scurlock, Scientific Advisory Board of the Turner Syndrome Society of t Research Registry et al.

Background Many different clinical specialists provide care to patients with Turner syndrome (TS), who have highly variable clinical manifestations. Therefore, a national TS registry is essential to inform a cohesive approach to healthcare and research. In 2015, the Turner Syndrome Society of the United States (TSSUS) created the Turner Syndrome Research Registry (TSRR) to engage directly with community participants who voluntarily provide longitudinal data about their experiences with TS. TSRR projects are collaborative partnerships between people with TS, TSSUS, and researchers. Results To ensure that registry workflows conform to the data privacy choices of participants, TSSUS collaborated with UTHealth Houston in 2021 to create a new version of the TSRR that completely separates participant health data (stored at UTHealth) and personal identifiers (maintained at TSSUS). We developed an innovative Visual Basic (VB) script that, when embedded into Microsoft Outlook, redirects REDCap surveys through TSSUS to participants by matching registry IDs to participant email addresses. Additionally, the utilization of REDCap allows for portability of data as it is an open source platform. Conclusion In this report, we will highlight three recent changes that more closely align the TSRR with this mission: a unique and equal collaborative partnership between UTHealth and TSSUS, an open-source platform, REDCap, that ensures data portability and compatibility across institutions, and an innovative survey routing system that retains participant confidentiality without sacrificing REDCap survey distribution capabilities to connect researchers with thousands of participants.

Kento Kawaharazuka, Shogo Makino, Masaya Kawamura et al.

Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.

Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

M. Sheppard, A. Wal, J. Banner et al.

Cardiomyopathies (CMP) comprise a heterogenous group of diseases affecting primarily the myocardium, either genetic and/or acquired in origin. While many classification systems have been proposed in the clinical setting, there is no internationally agreed pathological consensus concerning the diagnostic approach to inherited CMP at autopsy. A document on autopsy diagnosis of CMP is needed because the complexity of the pathologic backgrounds requires proper insight and expertise. In cases presenting with cardiac hypertrophy and/or dilatation/scarring with normal coronary arteries, a suspicion of inherited CMP must be considered, and a histological examination is essential. Establishing the actual cause of the disease may require a number of tissue-based and/or fluid-based investigations, be it histological, ultrastructural, or molecular. A history of illicit drug use must be looked for. Sudden death is frequently the first manifestation of disease in case of CMP, especially in the young. Also, during routine clinical or forensic autopsies, a suspicion of CMP may arise based on clinical data or pathological findings at autopsy. It is thus a challenge to make a diagnosis of a CMP at autopsy. The pathology report should provide the relevant data and a cardiac diagnosis which can help the family in furthering investigations, including genetic testing in case of genetic forms of CMP. With the explosion in molecular testing and the concept of the molecular autopsy, the pathologist should use strict criteria in the diagnosis of CMP, and helpful for clinical geneticists and cardiologists who advise the family as to the possibility of a genetic disease.

A. Maloberti, C. Giannattasio, M. Bombelli et al.

Harrison G. Zhang, J. Honerlaw, M. Maripuri et al.

The International Classification of Diseases (ICD)-10 code (U09.9) for post-acute sequelae of COVID-19 (PASC) was introduced in October of 2021. As researchers seek to leverage this billing code for research purposes in large scale real-world studies of PASC, it is of utmost importance to understand the functional use of the code by healthcare providers and the clinical characteristics of patients who have been assigned this code. To this end, we operationalized clinical case definitions of PASC using World Health Organization and Centers for Disease Control guidelines. We then chart reviewed 300 patients with COVID-19 from three participating healthcare systems of the 4CE Consortium who were assigned the U09.9 code. Chart review results showed the average positive predictive value (PPV) of the U09.9 code ranged from 40.2% to 65.4% depending on which definition of PASC was used in the evaluation. The PPV of the U09.9 code also fluctuated significantly between calendar time periods. We demonstrated the potential utility of textual data extracted from natural language processing techniques to more comprehensively capture symptoms associated with PASC from electronic health records data. Finally, we investigated the utilization of long COVID clinics in the cohort of patients. We observed that only an average of 24.0% of patients with the U09.9 code visited a long COVID clinic. Among patients who met the WHO PASC definition, only an average of 35.6% visited a long COVID clinic.

Deroma, the Emilia-Romagna, Emilia-Romagna et al.

Abstract Background Heart failure (HF) affected 64.3 million people worldwide and contributed to 9.9 million years lived with disability globally in 2017. Despite its global relevance, there is a lack of comprehensive statistics on the prevalence, incidence, and burden of HF in developing Central Asian countries. This study aims to fill the gap and present the data for Kazakhstan, the largest Central Asian country. Methods HF cases were identified through the Unified National Electronic Healthcare System records for 2014-2021 using the appropriate ICD-10 codes. Descriptive and survival analyses were used to present demographics, incidence, prevalence, and mortality rates. The calculation of DALYs is done according to the WHO methods. The information on comorbid conditions based on respective ICD-10 codes was collected by merging the databases using unique deidentifying patient numbers. Results During the observation period between 2014-2021 years, 501,663 patients with HF were identified, of them 52% were females, 86% were older than 50 years of age, and 58% were of Kazakh ethnicity. Hypertension, history of cerebrovascular diseases, and myocardial infarction were present in 40%, 34%, and 22% of the cohort, respectively. The age and sex-specific incidence show that women have higher incidence before 30 years of age compared to men. In addition, incidence rates for both sexes and all age categories decreased in 2021 compared to 2014. The prevalence dramatically increased from 4393 people per million population (PMP) to 22,088 PMP, while mortality rates changed from 367 to 721 PMP during the observation period. In 2021, 2,964,062 age and disability-adjusted life years (DALYs) were lost due to HF in Kazakhstan. More than 2 million DALYs belong to years of life lost (YLLs). Conclusions The DALYs show high economic and social loss due to high mortality among patients. Healthcare policymakers should prioritize the enhancement of cardiac services and the mitigation of its risk factors. Key messages • This study highlights the high burden of heart failure in Kazakhstan, with over 2 million DALYs lost in 2021 alone. Healthcare policymakers must prioritize cardiac services and risk factor reduction. • Lack of comprehensive data on HF in Central Asia made it difficult to tackle the burden. This study fills this gap, presenting valuable information for policymakers to reduce social and economic loss.